
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class     __UpperCamelCase :






     """simple docstring"""







     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	str					=		UNetaDConditionModel(
           sample_size=32     ,						layers_per_block=1     ,						block_out_channels=[32, 64]     ,						down_block_types=[
               '''ResnetDownsampleBlock2D''',
               '''SimpleCrossAttnDownBlock2D''',
           ]     ,						mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''     ,						up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']     ,						in_channels=3     ,						out_channels=6     ,						cross_attention_dim=32     ,						encoder_hid_dim=32     ,						attention_head_dim=8     ,						addition_embed_type='''text'''     ,						addition_embed_type_num_heads=2     ,						cross_attention_norm='''group_norm'''     ,						resnet_time_scale_shift='''scale_shift'''     ,						act_fn='''gelu'''     ,						)
       unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		DDPMScheduler(
           num_train_timesteps=1000     ,						beta_schedule='''squaredcos_cap_v2'''     ,						beta_start=0.00_01     ,						beta_end=0.02     ,						thresholding=_A     ,						dynamic_thresholding_ratio=0.95     ,						sample_max_value=1.0     ,						prediction_type='''epsilon'''     ,						variance_type='''learned_range'''     ,						)

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		IFWatermarker()

       return {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "watermarker": watermarker,
           "safety_checker": None,
           "feature_extractor": None,
       }



     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	str					=		TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		UNetaDConditionModel(
           sample_size=32     ,						layers_per_block=[1, 2]     ,						block_out_channels=[32, 64]     ,						down_block_types=[
               '''ResnetDownsampleBlock2D''',
               '''SimpleCrossAttnDownBlock2D''',
           ]     ,						mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''     ,						up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']     ,						in_channels=6     ,						out_channels=6     ,						cross_attention_dim=32     ,						encoder_hid_dim=32     ,						attention_head_dim=8     ,						addition_embed_type='''text'''     ,						addition_embed_type_num_heads=2     ,						cross_attention_norm='''group_norm'''     ,						resnet_time_scale_shift='''scale_shift'''     ,						act_fn='''gelu'''     ,						class_embed_type='''timestep'''     ,						mid_block_scale_factor=1.4_14     ,						time_embedding_act_fn='''gelu'''     ,						time_embedding_dim=32     ,						)
       unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		DDPMScheduler(
           num_train_timesteps=1000     ,						beta_schedule='''squaredcos_cap_v2'''     ,						beta_start=0.00_01     ,						beta_end=0.02     ,						thresholding=_A     ,						dynamic_thresholding_ratio=0.95     ,						sample_max_value=1.0     ,						prediction_type='''epsilon'''     ,						variance_type='''learned_range'''     ,						)

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	int					=		DDPMScheduler(
           num_train_timesteps=1000     ,						beta_schedule='''squaredcos_cap_v2'''     ,						beta_start=0.00_01     ,						beta_end=0.02     ,						)

       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	int					=		IFWatermarker()

       return {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "image_noising_scheduler": image_noising_scheduler,
           "watermarker": watermarker,
           "safety_checker": None,
           "feature_extractor": None,
       }



     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.get_dummy_components()
       __SCREAMING_SNAKE_CASE      :	str					=		self.pipeline_class(**_A )
       pipe.to(_A )
       pipe.set_progress_bar_config(disable=_A )

       __SCREAMING_SNAKE_CASE      :	int					=		self.get_dummy_inputs(_A )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		inputs['''prompt''']
       __SCREAMING_SNAKE_CASE      :	Dict					=		inputs['''generator''']
       __SCREAMING_SNAKE_CASE      :	str					=		inputs['''num_inference_steps''']
       __SCREAMING_SNAKE_CASE      :	List[str]					=		inputs['''output_type''']

       if "image" in inputs:
         __SCREAMING_SNAKE_CASE      :	Any					=		inputs['''image''']
       else:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		None

       if "mask_image" in inputs:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		inputs['''mask_image''']
       else:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		None

       if "original_image" in inputs:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		inputs['''original_image''']
       else:
         __SCREAMING_SNAKE_CASE      :	str					=		None

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	int					=		pipe.encode_prompt(_A )

       # inputs with prompt converted to embeddings
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		{
           '''prompt_embeds''': prompt_embeds,
           '''negative_prompt_embeds''': negative_prompt_embeds,
           '''generator''': generator,
           '''num_inference_steps''': num_inference_steps,
           '''output_type''': output_type,
       }

       if image is not None:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		image

       if mask_image is not None:
         __SCREAMING_SNAKE_CASE      :	Any					=		mask_image

       if original_image is not None:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		original_image

       # set all optional components to None
       for optional_component in pipe._optional_components:
         setattr(_A     ,						_A     ,						_A )

       __SCREAMING_SNAKE_CASE      :	Any					=		pipe(**_A )[0]

       with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(_A )
         __SCREAMING_SNAKE_CASE      :	Tuple					=		self.pipeline_class.from_pretrained(_A )
         pipe_loaded.to(_A )
         pipe_loaded.set_progress_bar_config(disable=_A )

       pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

       for optional_component in pipe._optional_components:
         self.assertTrue(
             getattr(_A     ,						_A ) is None     ,						F'''`{optional_component}` did not stay set to None after loading.'''     ,						)

       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.get_dummy_inputs(_A )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		inputs['''generator''']
       __SCREAMING_SNAKE_CASE      :	Tuple					=		inputs['''num_inference_steps''']
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		inputs['''output_type''']

       # inputs with prompt converted to embeddings
       __SCREAMING_SNAKE_CASE      :	List[str]					=		{
           '''prompt_embeds''': prompt_embeds,
           '''negative_prompt_embeds''': negative_prompt_embeds,
           '''generator''': generator,
           '''num_inference_steps''': num_inference_steps,
           '''output_type''': output_type,
       }

       if image is not None:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		image

       if mask_image is not None:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		mask_image

       if original_image is not None:
         __SCREAMING_SNAKE_CASE      :	str					=		original_image

       __SCREAMING_SNAKE_CASE      :	Tuple					=		pipe_loaded(**_A )[0]

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		np.abs(to_np(_A ) - to_np(_A ) ).max()
       self.assertLess(_A     ,						1e-4 )




     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		self.get_dummy_components()
       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.pipeline_class(**_A )
       pipe.to(_A )
       pipe.set_progress_bar_config(disable=_A )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.get_dummy_inputs(_A )
       __SCREAMING_SNAKE_CASE      :	int					=		pipe(**_A )[0]

       with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(_A )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.pipeline_class.from_pretrained(_A )
         pipe_loaded.to(_A )
         pipe_loaded.set_progress_bar_config(disable=_A )

       pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

       __SCREAMING_SNAKE_CASE      :	str					=		self.get_dummy_inputs(_A )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		pipe_loaded(**_A )[0]

       __SCREAMING_SNAKE_CASE      :	Any					=		np.abs(to_np(_A ) - to_np(_A ) ).max()
       self.assertLess(_A     ,						1e-4 )



import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard






def        snake_case__		(    lowerCamelCase_		):
							assert "mock" in _fsspec_registry
							assert "bz2" in _fsspec_registry






def        snake_case__		(    ):
							assert "mock" not in _fsspec_registry
							assert "bz2" in _fsspec_registry






def        snake_case__		(    ):
							A      :					List[Any]      		=					'''mock-s3-bucket'''
							A      :					Union[str, Any]      		=					F's3://{mock_bucket}'
							A      :					str      		=					extract_path_from_uri(lowerCamelCase_		)
							assert dataset_path.startswith('''s3://'''		) is False

							A      :					Tuple      		=					'''./local/path'''
							A      :					Tuple      		=					extract_path_from_uri(lowerCamelCase_		)
							assert dataset_path == new_dataset_path






def        snake_case__		(    lowerCamelCase_		):
							A      :					int      		=					is_remote_filesystem(lowerCamelCase_		)
							assert is_remote is True

							A      :					Dict      		=					fsspec.filesystem('''file'''		)

							A      :					Tuple      		=					is_remote_filesystem(lowerCamelCase_		)
							assert is_remote is False






@pytest.mark.parametrize('''compression_fs_class'''     ,				lowerCamelCase_		)
def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					{'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
							A      :					List[Any]      		=					input_paths[compression_fs_class.protocol]
							if input_path is None:
														A      :					int      		=					F'for \'{compression_fs_class.protocol}\' compression protocol, '
														if compression_fs_class.protocol == "lz4":
																					reason += require_lza.kwargs["reason"]
														elif compression_fs_class.protocol == "zstd":
																					reason += require_zstandard.kwargs["reason"]
														pytest.skip(lowerCamelCase_		)
							A      :					Dict      		=					fsspec.filesystem(compression_fs_class.protocol     ,				fo=lowerCamelCase_		)
							assert isinstance(lowerCamelCase_     ,				lowerCamelCase_		)
							A      :					str      		=					os.path.basename(lowerCamelCase_		)
							A      :					str      		=					expected_filename[: expected_filename.rindex('''.'''		)]
							assert fs.glob('''*'''		) == [expected_filename]
							with fs.open(lowerCamelCase_     ,				'''r'''     ,				encoding='''utf-8'''		) as f, open(lowerCamelCase_     ,				encoding='''utf-8'''		) as expected_file:
														assert f.read() == expected_file.read()






@pytest.mark.parametrize('''protocol'''     ,				['''zip''', '''gzip''']		)
def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Union[str, Any]      		=					{'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
							A      :					int      		=					compressed_file_paths[protocol]
							A      :					List[str]      		=					'''dataset.jsonl'''
							A      :					List[str]      		=					F'{protocol}://{member_file_path}::{compressed_file_path}'
							A		,		*A      :					List[Any]      		=					fsspec.get_fs_token_paths(lowerCamelCase_		)
							assert fs.isfile(lowerCamelCase_		)
							assert not fs.isfile('''non_existing_''' + member_file_path		)






@pytest.mark.integration
def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[Any]      		=					hf_api.dataset_info(lowerCamelCase_     ,				token=lowerCamelCase_		)
							A      :					List[str]      		=					HfFileSystem(repo_info=lowerCamelCase_     ,				token=lowerCamelCase_		)
							assert sorted(hffs.glob('''*'''		)		) == [".gitattributes", "data"]
							assert hffs.isdir('''data'''		)
							assert hffs.isfile('''.gitattributes'''		) and hffs.isfile('''data/text_data.txt'''		)
							with open(lowerCamelCase_		) as f:
														assert hffs.open('''data/text_data.txt'''     ,				'''r'''		).read() == f.read()






def        snake_case__		(    ):
							A      :					Tuple      		=					'''bz2'''

							# Import module
							import datasets.filesystems

							# Overwrite protocol and reload
							register_implementation(lowerCamelCase_     ,				lowerCamelCase_     ,				clobber=lowerCamelCase_		)
							with pytest.warns(lowerCamelCase_		) as warning_info:
														importlib.reload(datasets.filesystems		)

							assert len(lowerCamelCase_		) == 1
							assert (
							    str(warning_info[0].message		)
							    == F'A filesystem protocol was already set for {protocol} and will be overwritten.'
							)






